Accuracy of a Model can be improved in several levels and multiple variables, boundaries and guidelines. With the well known problem statement and solution, it is difficult to evaluate for all the given cases the model would be predicting expected outcomes. Machine Learning Models are solving for the problems for which results are unknown, most of the times. This arises a problem of Test Oracle. Recent surveys and work have shown that this difficulty can be reduced by some of the blackbox testing techniques such as Metamorphic Testing, Fuzzing, Dual Coding et.,

Even though the output of a Model is not known, we can make few predictions based on the Metamorphic relations. A metamorphic relation refers to the relationship between the software input change and output change during multiple program executions. Many metamorphic relations are created based on the transformation from training data set or test data set. We further classify them into Coarse-grained Data transformation and Fine-grained data transformation.

We will discuss different transformations. Will go through the example of a Search relevancy problem and will analyse the application of Metamorphic testing to verify the Machine model built.